{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#绘制多折线图\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "#导入文件\n",
    "df1=pd.read_excel('C:\\\\Users\\\\Administrator\\\\Desktop\\\\datas.xls')\n",
    "\n",
    "#多折线图数据准备\n",
    "x1=df1['地区']\n",
    "y1=df1['图书']\n",
    "y2=df1['百货']\n",
    "y3=df1['食品']\n",
    "\n",
    "#格式设置\n",
    "plt.rcParams['font.sans-serif']=['SimHei']   #解决中文乱码\n",
    "plt.rcParams['xtick.direction'] = 'out'      #x铀的刻度线向外显示\n",
    "plt.rcParams['ytick.direction'] = 'in'       #y铀的刻度线向内显示\n",
    "plt.title('各地区销售额对比',fontsize='18')  #图表标题\n",
    "plt.grid(axis='y')                           #显示网格隐寂y铀网格线\n",
    "plt.ylabel('销售额')                         #y铀标签\n",
    "plt.yticks(range(200,700,50))                #y铀刻度\n",
    "\n",
    "#3条折线图绘剖\n",
    "plt.plot(x1,y1,label='图书', color='r',marker='p')\n",
    "plt.plot(x1,y2,label='百货', color='g',marker='.',mfc='r',ms=8, alpha=0.7)\n",
    "plt.plot(x1,y3,label='食品', color='b',linestyle='-.',marker='*')\n",
    "\n",
    "#图例\n",
    "plt.legend(['图书','百货','食品'])\n",
    "#图表展示\n",
    "plt.show ()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#绘制柱形图\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "#导入文件\n",
    "df=pd.read_excel('C:\\\\Users\\\\Administrator\\\\Desktop\\\\datas.xls')\n",
    "\n",
    "#柱形图数据准各\n",
    "x=df['地区']\n",
    "height=df['销售额']\n",
    "\n",
    "#格式设置\n",
    "plt.rcParams['font.sans-serif']=['SimHei'] #解决中文乱码\n",
    "plt.grid(axis='y', which='major') #生成虚线网格\n",
    "plt.xlabel('年份') #x铀标签\n",
    "plt.ylabel('销售额(万元)')#y轴标签\n",
    "plt.title('各地区销售额分析') #图表标愍\n",
    "\n",
    "#柱形图绘制\n",
    "plt.bar(x,height,width = 0.5,align='center',color = 'b' ,alpha=0.5, bottom=0.8)\n",
    "\n",
    "#设置每个柱子的文本标签, format (b,',')格式化销售额为千位分隔符格式\n",
    "for a,b in zip(x, height ) :\n",
    "    plt.text (a,b,format (b,','), ha='center', va= 'bottom',fontsize=9,color = 'r', alpha=0.9)\n",
    "    \n",
    "#图例\n",
    "plt.legend(['销售额'])\n",
    "\n",
    "#图形展示\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#绘制柱形图\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "#导入文件\n",
    "df=pd.read_excel('C:\\\\Users\\\\Administrator\\\\Desktop\\\\datas.xls')\n",
    "\n",
    "#柱形图数据准各\n",
    "x=df['销售额']\n",
    "\n",
    "#格式设置\n",
    "plt.rcParams['font.sans-serif']=['SimHei'] #解决中文乱码\n",
    "plt.xlabel('销售额') #x铀标签\n",
    "plt.ylabel('地区数量')#y轴标签\n",
    "plt.title('各地区销售额分布直方图') #图表标愍\n",
    "\n",
    "#直方图绘制\n",
    "plt.hist(x,bins=[1000,1200,1300,1400,1500],facecolor=\"blue\",edgecolor=\"black\",alpha=0.7)\n",
    "\n",
    "#图形展示\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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ak5+XuPuSbh5wGPAr4OxC5JeKdzpwkBnHubMg0wJmdilwAOHvdzBwuZlBOHTyNcAjwCYzGwo0A+1mNgjYVID8Im+iQiAxtaZ/NbN9CCv97QhvoAA/ZMsugoOA1RkfyWw8cDfwn3nKKpLJ7sCTZhzvzr8y3D4OONvdl3W9wUKBvR5oA/4EPEg4dPakvKUVyUKFQArKzLYlHAeghfApCWC4mX0P2I1wwpmpwCvA8rS7bgN81N1nZXjQnYD7CSMLIoU2AXjIjDPcua3LbR3AkG7utyfhAFm7Ak8C7yEUg17PTxAZCJpDIAXl7qvc/Wh3Pw7YkFy9LTDd3c909xWET0xz3P29nRfgzowPaHYA4c1UZUBiGgbcbMZXulxfTfh7zmQt4cyaw4HfAI8CDpyZn4gi2akQSDFY4u7Ppf1cBUw0s0fN7FUzexQ4nDCqsIXZUcBjhEIhEtsg4AozLk27bjLdbOZy9+XAa0CVu7elXd+c15Qi3VAhkCjMbDu6DKVaMtuK8ClpEXAqMBM4nnBGwtq0hU8C7gVG5z+tSK9804xrzUbtCgx196Ysy54H/D35fgjoTIsSj+YQSEFZ+FT/LWABW/7+xpnZYOBdZvYRwhEFb3D31WZm7r7BzB4CHjKzEx2OBv6I/n6leH0Ovr0//Oyn3S2QTCo8AvhwctV1hDIMYROESEHpDVUK7QngWHdvNbN7k+ueBB4gbGv9GlDj7o+b2cXARgB3v83MljTAKcCv0VkKpehdcDBccI4Z0915yyiBu28ysyPdvT35uT3ttuMKmVQEVAikwNx9Y9r3xyZfL+hm2e++6Wc4Evh5XgOKDKxjgDvNONa9yxwY3lwCRGLTHAIpDWZnozIgpeko4C9mer+V4qY/UCl+ZicAV8eOIdIPpxI2dYkULRUCKW5m7wZuQLOvpfR90YyLYocQ6Y4KgRSvcMbCuwkHbhEpBz8w49zYIUQyUSGQ4mQ2CZgG1MSOIjLArjTj47FDiHSlQiDFx6yGUAZ2jB1FJA8M+JMZx8QOIpJOhUCKi9lw4B7gLeeNFykjQ4BbzTg0dhCRTioEUjzMqoCb0CmMpTKMAO41Y5fYQURAhUCKy+XAf8UOIVJA44FbzHSoYolPhUCKQziHwZdjxxCJYH/gl7FDiKgQSHxmOwN/iB1DJKLPmfGJ2CGksqkQSFxmQ4AbgXGRk4jEdrWZJtNKPCoEEtslwDtjhxApAiMJ8wlGxg4ilUmFQOIxOx44L3YMkSLyduDa2CGkMqkQSBxmk4HrY8cQKUKnm/GF2CGk8qgQSOFtmTegwxKLZHaFGQfGDiGVRYVAYrgYdIQ2kSyqgalmjIgdRCqHCoEUltlRwNdjxxApAbsAF8YOIZVDhUAKx6wa+A3h5C4i0rPzzNg3dgipDCoEUkjfBnaLHUKkhAwGrjHTe7Xkn/7IpDDMdgHOjx1DpAQdCtrrQPJPhUAK5degE7iI9NHFZmwXO4SUNxUCyT+zk4FjYscQKWFjgStjh5DypkIg+WU2CrgidgyRMvBRMz4cO4SULxUCybcLgR1jhxApE1fp2ASSLyoEkj9m+wBfiR1DpIzshI5NIHmiQiD5YWbAbwm7TYnIwPmKGW+LHULKjwqB5MtHgHfHDiFShoYC34sdQsqPCoEMvDA6cEHsGCJl7DNm7BQ7hJQXFQLJh+OB/WKH6I+ZwHuAwwjnaP4TcBbwcWBDlvv9Dbg8+f5i4LPJ99PzEbJizKRvv431bBmkKrvfxhDg+7FDSHlRIZB8KPnRgRnAHcBDwLWE1c4fCIeMe6Sb+6wirK46Z1EuIfwHewXYPn9RK0BffhsA32LLW1xZ/jY+bcaU2CGkfKgQyMAyOxb4j9gx+uszwATgaeBg4FzgTuBh4Ihu7vMVoAE4BZgDfBDYHXgJ2CfPectbX34b9wI7E04YCGX62xiMRglkAKkQyEAr+dGBTvXAj9iyj9dewGjCZ82uNgDPA48DlxLepU8BzgAWA98AmvIbt8z15rexnjBW862068r2t/FJs82tR6RfVAhk4JgdDRwSO8ZAaCF8Lr0UqCLsP7krcBTwcoblq4E9CBt2RyX3AfgXYZXV+dlU+qK3v42HCL+RrwPPANck15flb2MwZVTCJS7tIy4D6QexAwyUi4EFwFWEz6EOnEZo0NcQBq/nAmcmy1cD70t+/jdwGWHzwQTCIPd04MQCZS8/vf1tnJxcSK77PGX+2/iEGT9xZ0HsIFLazN1jZ5ByYHYk4aOZlKAr+fKTX+XKd8XOIX32V3c+GTuElDZtMpCBUjajAyIl6GM6PbL0lwqB9J/ZwXQ/3VtE8m8wYaKFSJ+pEMhA+GzPi4hInp1lhsUOIaVLhUD6x2w44ZBxIhLXFOD9sUNI6VIhkP76CDA2dggRAeBzsQNI6VIhkP7SdkuR4nGiGRNjh5DSpEIgfWc2GTgydgwR2Wwo8OnYIaQ0qRBIf5wJmsQkUmTOjh1ASpMKgfSNmbHl0HAiUjz2NOM9sUNI6VEhkL56H7BT7BAikpEmF0qvqRBIX+nYAyLF6xQzRsUOIaVFhUB6z2wMYXdDESlOw4EPxA4hpUWFQPriGMIbjogUrw/HDiClRYVA+uKDsQOISI9UCKRXVAikL1QIRIrf9mYcEDuElA4VAukds72BHWLHEJGcHBs7gJQOFQLpLY0OiJQObTaQnKkQSG8dEzuAiOTsEJ3bQHKlQiC5MxsBOgKaSAkZhEq85EiFQHrjCKA6dggR6RXNI5CcqBBIb+iThkjp+aAZVbFDSPFTIZDe0IRCkdJTA/xH7BBS/FQIJDdmk4E9YscQkT5RIZAeqRBIrg6JHUBE+uzA2AGk+KkQSK72jR1ARPpMRyyUHqkQSK5UCERK195mDIkdQoqbCoHkar/YAUSkz6qBvWKHkOKmQiA9MxsLvC12DBHpF80jkKxUCCQX2lwgUvo0j0CyUiGQXKgQiJQ+jRBIVioEkgsVApHSt5+Z3vOle/rjkFxoQqFI6RuJDi4mWagQSHZmBuwdO4aIDAiN9km3VAikJ7sQPlmISOmbHDuAFC8VAunJ7rEDiMiA0e7D0i0VAunJDrEDiMiAmRQ7gBQvFQLpyfaxA4jIgNEIgXRLhUB6sl3sACIyYDRCIN0aHDuAFD2NEIiUDG9iUGsd1Q31jFjTyJgVrYxb7ExYOJQJ84dTs3iMXfTiME/5pthJpfioEEh2U6hmFU00MSJ2FJHK5W9Q1VLH0MZ6RtS9wZhXW6lZAuMXDGHCguHULBrHuGUTGLZhDD3vSbA1sLwAoaXEqBBIdovYExiB8wZtrKOJeuppYg3NrKSd5cByhrCUYSxjFK8yltcYT6tOtSrSM28MK/qGDYzsXNEvhvELhzBh/ghqFo9l3LKJVDeMZuB2/90GFQLJQIVAejIeAGMkQxjJWHak89yH2Y6M3kE9razlDRpYRxOraWUF7dQyiKUMYSkjWM4oVlDDGmpwrBAvRqQwfANVza9T3bCeEXUbGVubrOgXDGHCwpHULBrL2OUTqW4cBYwqcLhtCvx8UiJUCCQLG0Jf36wGMZZqxlJNqBS7ZFnWaaeDdTSzjkYaWctGVtHGq3SwnCqWUs0yRrCcMaykhg2M7lMmkX7z+rCi31DPyLqNjKltYfxiS4buR4UVfe1Ehr4xBhgTO203ijWXRKZCINnUFORZjCqqmMgIJjKCsIVzzyzLO820s5aN1LOBRl6nmX/TRi3OMoYkBWIkrzKWfzOeTQwryOuQEtZRT1XLGobV12/5RL9oEBMWDGH8wpGMXzSWMbVbMbRpLDA2dtp+0nwgyUiFQLIZHztARkY1g9mO0WzHaMKhk7Idod1ppJW1NLGBeppYncx/qGUQyxjMUoazjFGsYByvMZ52qgr0SiTvOtYzuPMT/eqNjF3eSs3iZEW/YBQ1i8cytnYrhmwshxV9rlQIJCMVAsmmPIbmjVEMZRRDgXFkn4PtOM46WljHGzSwlo2spoUVdLCcQSxlaDL/YTQrqWEtYzX/IYaOdQzetIbqDQ2Meq2JsbXt1CyCCQuqGb9gJDVLxjGmdiuGbBpH+K3LFioEkpEKgWRTeSs6wzBqGEYNw4AJwG5Zlnfa6GAtm1hPA42sZROraKUWZzmDWcpQljGSWsawkvE06kRR2XWsZfCm1xlWv4GRr21k7PJ2xi+C8QuqmbBgJDVLxjPm1YkMbq6hUJu0yo8KgWSkQiDZeOwARc8YTBVbM5KtGQlsC7wjy/LORto2z394Y/Pum7WQbL4YxnJGUss4VjGeFoYW5oXkkzt4sqJf38CozSt627yiH7ekhjGvbsXglvEU66aq8qFCIBmpEEg2HbEDlB1jOEPYgSHswBhgR2D/LMs7G2hhHU1sYD1NrKZl8/yHsPvmcJYzilcZRx3j6Sjk4cjdsY7XN6/oR762kXHLO6hZaGHofuEoapIVfVXrBMJ4i8SnUSrJSIVAstEIQWzGGKoZQzVhgHznLMs6HTiv08x6GmlgHRtZRWsy/6Fq8/yHWkazgvGs7273M+8IK/qNaxm2fgOjVjUzbnlbMut+GOMXjGTc0vGMWTGRqtaJwMSBf+GSR5o0KxmpEEg2KgSlxBiEMYHhTGA4sBWwe5blnRbaWcsm6l/70911PPfiHGoW1zDq31tR1bZV8ghSflpjB5DipEIg2agQlDNjKIPZllFse1/Vkjp2WpJt9oOUDxUCyUinP5ZsVAgqxJw6nRa3gqgQSEYqBJKNJhVWgBUbWN3a0eMZ8qR8qBBIRioEko1GCCrA7fNYHDuDFJQKgWSkQiDZqBBUgFvn0hI7gxSUft+SkQqBZKM3jgrw3ErtTVBhmmMHkOKkQiDZrI4dQPKroZnGxpasOydK+Xk9dgApTioEkoXXAxtjp5D8uX8h89GBaipNXewAUpxUCKQnq2IHkPyZOpuG2Bmk4NbEDiDFSYVAeqJCUMYeX9bd4YuljKkQSEYqBNITFYIy1dpOW12T5g9UIBUCyUiFQHqiQlCm/rmc+ejMd5Wm1VO+PnYIKU4qBNITFYIyNXW2JpdVII0OSLdUCKQnKgRlavoihsXOIAW3NHYAKV4qBNITFYIytayeKbEzSMEtjB1AipcKgfREhaAMvfwaSzpcRyisQCoE0i0VAunJ0tgBZODdPIcVsTNIFCoE0i0VAumBrwZei51CBtZdr8ROIJEsih1AipcKgeTixdgBZGDNXcOk2BkkCo0QSLdUCCQXKgRlZGUDdS3tTI6dQwpuvadcJzaSbqkQSC5mxg4gA+e2uRo2rlAvxQ4gxU2FQHKhEYIycutcWmJnkCiejx1AipsKgeTiFaA5dggZGM+t1O6GFUqFQLJSIZAceBswO3YK6b+GZhobW3RCowqlQiBZqRBIrrTZoAzcv5D5QFXsHFJwDYSRPpFuqRBIrmbGDiD9N3U2DbEzSBQzPOUeO4QUNxUCyZVGCMrA48sYEzuDRKHNBdIjFQLJ1bNoYmFJa22nra5J8wcq1BOxA0jxUyGQHHkT8M/YKaTv/rmc+cDI2Dmk4DqAR2KHkOKnQiC9MT12AOm7qbOpi51BopjhKV8bO4QUPxUC6Y1psQNI301fxLDYGSSKh2IHkNKgQiC94C8DK2OnkL5ZVs+U2BkkChUCyYkKgfTW32MHkN6btZolHa4jFFagZuAfsUNIaVAhkN7SPIISNHU2K2JnkCie9JRvjB1CSoMKgfTWA4RZy1JC7tIx6irVfbEDSOlQIZBe8tfRQU5Kztw1TIqdQaK4JXYAKR0qBNIX2tughKxsoK6lncmxc0jBveApXxI7hJQOFQLpi3tjB5Dc3TaXRbEzSBQaHZBeUSGQPvCngfmxU0hubp1LS+wMEoUKgfSKCoH01fWxA0hunlup3Q0r0Mue8gWxQ0hpGRw7gJSsPwM/psRK5cyZcO654A5nnw0vvABz54bbbrsNRo9+8/J1dXD++eH7NWtg0iTYbjtYuBCuuw6mT4cPfrCgL6FXGpppbGzRCY0qkEYHpNdK6s1ciomvAB6MnaK3ZsyAO+6Ahx6CSy6BAw+EBx6Ad78bHslw+pettoLf/z5c9twTzjsPliyBQYPglVdg++0L/hJ65f6FzAeqYueQgnLgb7FDSOlRIZD++GPsAL31mc/AhAnw9NNw9NFw5pnh+mefhYMP7v5+q1ZBUxNMmRJGBHbfHV56CfbZpyCx+2zqbBpiZ5CC+4enfGHsEFJ6VAikP+4A1kfO0Gv19fCjH8GFF4aff/MbOOqosCmgO1ddBWedFb4/5RQ44wxYvBi+8Y1QFIrV48sYEzuDFNx1sQNIaVIhkH7wTcBNsVP0RktLGCW49FKoqYG//z2MDpx3Xvb7zZ4N++235ed//StsOugcKShGre201TVp/kCF2QDcHDuElCYVAumvktpscPHFsGBB+MR/3nnw0Y9CW1uYYPjII2FTwvXXv/k+s2fDDjts+bmhIWx2OPhgePDBsBmhGD1Ry3xgZOwcUlB/8ZQX8ZiVFDNz99gZpOTZHODtsVPIm33xHh67+nmOiJ1DCmpfT/nLsUNIadIIgQwEbbMsQtMXMSx2BimoJ1QGpD9UCGQg/A6ojx1C3mxZPUW6MUPy5PLYAaS0qRDIAPB64NexU8gWs1azpMN1hMIKMp+w149In6kQyEC5AngjdggJps5mRewMUlCXeco7YoeQ0qZCIAPE1wDXxk4hwV2vxE4gBbSScChxkX7RuQxkIP0c+G+gOnaQSjd3DZNiZxgQG4FpQAewW3LdS8A44MNk/kgzE1gGtALHAc8Aa4ETgYXArnnMG8cVnnKd0VL6TSMEMoB8JToLYnQrG6hraWdy7BwDohZ4D3Ay8AJh5X4GsCOwKMPyHUAzcEKyzFLCsTQNWAOMznCf0rYeuDp2CCkPKgQy0H4GtMUOUclum5txVVmadgeGAfcBOwFbE1bu2xM+9Xc1CDgEmAcsASYDuwATgNeAbfKeuNCu8pTrfBUyIFQIZID5UnSmtahunUt5DR9XE1bs9Wx5x3LCSEB3tgKGEj4/7wXsC6wDpkMZ/eu8DlwWO4SUDxUCyYefEgZvJYLnVpbR7oYvEuYR7EVYoW8A2oEVQE2G5TcBzxJGBKYQRgUgbHpYx5aRgvJwsadcx/+QAaNCIHng84CpsVNUooZmGhtbyuiERlsDdxJOoXUgYXPAzYRJg28HXgVmpC0/jDBX4GbCHIM9CSMJw4EdgMVkLhKlZxlwVewQUl50LgPJE5tE2JI7InaSSjJ1Ni+cdgsHxs4hefdpT7l2NZQBpRECyROvBX4SO0WlmTobTTArfy8Bf40dQsqPCoHk08+BBbFDVJLHlzEmdgbJu/N1VELJBxUCySNvAb4cO0WlaG2nra6pjOYPSCb3eMrvix1CypMKgeSZTyNMC5M8e6KW+cDI2Dkkb5qAL8UOIeVLhUAK4auEncckj26aRV3sDJJXF3nKl8UOIeVLhUAKwJcCl8ROUe6mL2JY7AySNy8Dl8cOIeVNhUAK5RLCXuCSJ8vqmRI7g+SFA5/3lOuQ4JJXKgRSIL6JsOlA8mDWapZ0eBkdoVDS/d5T/mTsEFL+VAikgPxu4IbYKcrR1NmsiJ1B8qIW+FbsEFIZVAikV8ysysysy3VmZlU5PsR/A8sHPlllu+uV2AkkDzqAT3nK18cOIpVBhUB66+vAg2b2oJnVmdmDwIPA2UlZ+ImZ7W1mN5nZA2a2wsymmdkvw919PfBpdPKjATV3DZNiZ5AB9wtP+aOxQ0jl0LkMpM/M7B53P67LdeOAw4FVhJV+CrgIuBA4192XJEteCnyzcGnL18oG6na4XPMHysxM4BBPefmcrFmKnkYIJGfZNg2Y2SAzOwm4lHCC2Z2BnYBRaV8npt3l+7z5PHXSR7fNZVHsDDKgNgFnqAxIoQ2OHUBKyjuAa8ysjVAmDzOzR5Pbqgif+F8AlgLfAcYDexCOrtbs7s9ueShvATs1WX50YeKXp1vnohVHefm2p3xO7BBSeVQIJGfuPhs4DMDMjgW2Ay5z93s7l0kmHO4CXAu0EkrCZUCVmQ1yTz8piy8E+xxwY6FeQzl6bqU2F5SR24BfxQ4hlUmbDKTXzGwYYVeoo4FvmNnYtJu/BqwG2oB2wkFV2pKvGf7e/Cbg6vwmLl8NzTQ2tuiERmViDvBpT2lil8ShEQLpFTObCFwPXOfuS8zsO8C9ZnY2MAGoA94NnE4oAfsQNh8MIvy9ZTpT23nAocD++c5fbqYtZD5wYOwc0m/1wEme8sbYQaRyaS8DyZmZHU/Ya+AC9y2nYDWzvYGrgOuAJ9x9YXJ9NfAHd/9EDo/+NuApwmYIydEpU3ns1rkcETuH9IsDx3vK74kdRCqbCoHkzMyGA+3u+Zr9bAcAjxP2SJAcbH0ZM+qaOCB2DumXCz3lF8UOIaI5BJIzd9+YvzIA4DOAUwlzD6QHre201TVp/kCJuxP4YewQIqBCIEXH7wf+J3aKUvBELfOBkbFzSJ89DZyuSYRSLFQIpAj5NYTTJUsWN82iLnYG6bNXgGM95U2xg4h0UiGQYnU+Oj5BVtMXMSx2BumTfwPHeMpfjx1EJJ0KgRQpd+BM4B+RgxStZfVMiZ1Beq2eUAaWxg4i0pUKgRQxbwZOJAyvSppZq1nS4TpCYYlpBk70lL8UO4hIJioEUuR8LeGIiDqBT5qps1kRO4P0Sitwmk5nLMVMhUBKgC8nnFJ5buwkxeIujZmUkhbgZE/5nbGDiGSjQiAlwlcCRwAvxk5SDOauYVLsDJKTZsIhie+OHUSkJyoEUkK8Dngf8EzsJDGtbKCupZ3JsXNIjzYR5gxkOn+HSNFRIZAS4+uAo6jgvQ9un6f5FCVgI3CCp3xa7CAiuVIhkBLkDcAxwAOxk8RwyxzyePhoGQAbCAcd+juAmW1rZod0XcjMqtK+1xkrJToVAilR3gT8F1Bx22afW6ndDYvYv4HDPeWPpF03BfhI+kJmNgqYbmZHm9mOwJ/M7BPJ5dNmpk1CUnAqBFLCvBk4GbgpdpJCaWimsbFFJzQqUq8A/+kp7zrxtYNwiuPN3L2RUBIMOBf4NjAvucwFGvKeVqSLwbEDiPSPt4J9HJhFOGucRQ6UV9MWMh/Q8HLxeYywN8G6bm4/3cwOJXwIW0c4LHcjYeX/feBYoCm5LHf3T+U/ssibaYRAyoA7+I+B4wmHhi1bN83WJ8ci9Gfg6CxlAOBv7v5edz/c3U8gbOp6HzAcuJgwQnAnYcLsOjPTe7MUnEYIpIz4PWDvJLyx7hk7TT48vowxsTPIZu3A+Z7yy3p7R3dvNLNLgUnAFYTRgmbgA8CuwFPADQMXVaRnaqFSZnw+cAihFJSV1nba6po0f6BIvAa8vy9lIM3XCKXif4FPAo8DHwL+7O4qA1JwKgRShnwDcBJwIV0mc5WyJ2qZD4yMnUN4AjjQU/5YdwuYWZWZZZzPYsEOwBR3fwFoA64GDgUeIcw32CUPuUWyMveyeb8UycCOB/4CpT/U/sV7eOzq5zkido4KdwXwTU95W7aFzOxs4ONkLqQGTAVmuPszXe73F+CP7v7wwMQVyZ0KgVQA25Owa+K+sZP0x5QreXrJet5ygBspiHrg857yvO7iambmelOWSLTJQCqAzwMOAn5EGJ4tScvqmRI7Q4V6CNgn32UAQGVAYlIhkArhreA/AN4JvBQ7TW/NWs3SDtcRCgusiXDQoA94ymtjhxHJN+12KBXGZ4AdRDgYzPnAkMiBcnLzHGqBnWLnqCBPAZ/ylC+IHUSkUDRCIBXIW8FThNGCroeZLUp3zivvIzAWkWbge8BhKgNSaTRCIBXMZ4IdTFgBfJciHi2Yu4ZJsTNUgGnAuZ7yhbGDiMSgvQxEALD9gGug+Gbxr2ygbofLNX8gj5YB53nKb48dRCQmbTIQAcBfBD8U+ChQVEPFt89jUewMZaqFcB6Bd6gMiKgQiHThtwB7AV8CVkcOA8Atc2iJnaEM3Q3s7Sn/nqe8KXYYkWKgTQYi3bLRwDcJx5yPdsjg0T9lbmMLb4/1/GXmYeC7nvKnYwcRKTYqBCI9sm2Bi4CzgKpCPnNDM41jfsbwQj9vGXoa+J6n/KHYQUSKlTYZiPTIV4F/HtgbuJ0CnjBp2kLmozLQHy8DJ3rKD1UZEMlOhUAkZz4P/CPAnsBVhHPY59VNs2nI93OUqYeBDwP7ecrL7lTYIvmgTQYifWZjCZsRziVPRxHc+jJm1DVxQD4euwy1AjcCl3vKZ0bOIlJyVAhE+s2qgOOBr8DAnZ64tZ22oT+mmYgTGkvEeuBa4Jee8hWRs4iULB2pUKTfvJ0wt+B2sP0JxeDjQHV/HvWJWuYD7+h3vPLUQTgL4R+B2z3lmyLnESl5KgQiA8pnAp8B+xZwCuFAR4fTh4mBU2dTN7DZysJi4Hrgep2BUGRgaZOBSN7Z1sBHCOXgCHIsB1Ou5Okl64vvUMoRvAbcBfwNeMxTetMSyQcVApGCsq2Bkwjl4L1kKQdVP6Suwyv2HAYLgTsIm2Ke8pR3xI0jUv5UCESisa0I5eB44D3AmM5bZq1m6T6/zc+eC0WqDXgWuA+4w1M+K3IekYqjOQQi0XgdYXb8tcmeCgcSRg3ed8MshpCnXRmLRDswA3iccMyAxz3lOuaCSEQaIRApQnaRVRGOjPifyeUgYDdK86iFDiwBZgIvAk8CT3rK835gJxHJnQqBSImwi6yacJTEfQhlofMyOWauLtYQ9gSYRSgAM4EXPeUbImYSkRyoEIiUOLvIRgA7ApPSvnZedgDGEeYnjKZvIwwONADr0i6vA8sIn/yXdl485W/0+YWISFQqBCIVxC6y4WwpB6MJ84iccKCfji7fbyIcBXC9p7w9Rl4RKRwVAhEREdHZDkVERESFQERERFAhEBEREVQIREREBBUCERERQYVAREREUCEQERERVAhEREQEFQIRERFBhUBERERQIRARERFUCERERAQVAhEREUGFQERERFAhEBEREVQIREREBBUCERERQYVAREREUCEQERER4P8BOnHbvr9KVNcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 648x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#绘制柱形图\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "#导入文件\n",
    "df=pd.read_excel('C:\\\\Users\\\\Administrator\\\\Desktop\\\\datas.xls')\n",
    "\n",
    "#饼图数据准备\n",
    "labels=df1['地区']\n",
    "sizes=df1['销售额']\n",
    "\n",
    "#格式设置\n",
    "plt.rcParams['font.sans-serif']=['SimHei']#解决中文乱码\n",
    "plt.figure(figsize=(9,5)) #设骂画布大小\n",
    "colors = ['red', 'yellow', 'green', 'blue']#设置饼形图每块的颜色\n",
    "\n",
    "#饼图给剖\n",
    "plt.pie(sizes,#绘图数据\n",
    "labels=labels,#填加区域水平标签\n",
    "colors=colors,#设置饼图的自定义填充色\n",
    "labeldistance=1.02,#设置各各扇彤标签（图例)与圆心的距离\n",
    "autopct='%.1f%%', #设置百分比的格式,这里保留一位小数\n",
    "startangle=90,#设置饼图的初始角度\n",
    "radius = 0.5,#设置饼图的半径\n",
    "center = (0.2,0.2),#设置饼图的原点\n",
    "textprops = {'fontsize':9, 'color':'k'},#设置文本标签的属性值\n",
    "pctdistance=0.6)#设置百分比标签与蹰心的距离\n",
    "plt.axis('equal')#设置x，y轴刻度一致，保证饼图为圆彤\n",
    "plt.title('2021年8月各地区销里占比分析')#标题\n",
    "\n",
    "#图形展示\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#绘制柱形图\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "#导入文件\n",
    "df1=pd.read_excel('C:\\\\Users\\\\Administrator\\\\Desktop\\\\datas.xls')\n",
    "\n",
    "#致点图数据准各\n",
    "x1=df1['地区']\n",
    "y1=df1['图书']\n",
    "y2=df1['百货']\n",
    "y3=df1['食品']\n",
    "\n",
    "#格式设置\n",
    "plt.rcParams['font.sans-serif']=['SimHei']   #解决中文乱码\n",
    "plt.rcParams['xtick.direction'] = 'out'      #x铀的刻度线向外显示\n",
    "plt.rcParams['ytick.direction'] = 'in'       #y铀的刻度线向内显示\n",
    "plt.title('各地区销售额对比',fontsize='18')  #图表标题\n",
    "plt.grid(axis='y')                           #显示网格隐寂y铀网格线\n",
    "plt.ylabel('销售额')                         #y铀标签\n",
    "plt.yticks(range(200,700,50))                #y铀刻度\n",
    "\n",
    "#散点图给制\n",
    "plt.scatter(x1,y1,color='r')\n",
    "plt.scatter(x1,y2,color='g')\n",
    "plt.scatter(x1,y3,color='b')\n",
    "\n",
    "#图例\n",
    "plt.legend(['图书','百货','食品'])\n",
    "\n",
    "                                         \n",
    "#图形展示\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "The number of FixedLocator locations (5), usually from a call to set_ticks, does not match the number of ticklabels (4).",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-18-7649ca32ad45>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     25\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     26\u001b[0m \u001b[1;31m#设置角度网格标签\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 27\u001b[1;33m plt.thetagrids(angles*180/np.pi,\n\u001b[0m\u001b[0;32m     28\u001b[0m \u001b[0mlabels\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     29\u001b[0m fontproperties='simhei')\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\matplotlib\\pyplot.py\u001b[0m in \u001b[0;36mthetagrids\u001b[1;34m(angles, labels, fmt, **kwargs)\u001b[0m\n\u001b[0;32m   1858\u001b[0m         \u001b[0mlabels\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0max\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mxaxis\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_ticklabels\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1859\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1860\u001b[1;33m         lines, labels = ax.set_thetagrids(angles,\n\u001b[0m\u001b[0;32m   1861\u001b[0m                                           labels=labels, fmt=fmt, **kwargs)\n\u001b[0;32m   1862\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0mlines\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlabels\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\matplotlib\\projections\\polar.py\u001b[0m in \u001b[0;36mset_thetagrids\u001b[1;34m(self, angles, labels, fmt, **kwargs)\u001b[0m\n\u001b[0;32m   1344\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_xticks\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mangles\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1345\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mlabels\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1346\u001b[1;33m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_xticklabels\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1347\u001b[0m         \u001b[1;32melif\u001b[0m \u001b[0mfmt\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1348\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mxaxis\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_major_formatter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmticker\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mFormatStrFormatter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfmt\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\matplotlib\\axes\\_base.py\u001b[0m in \u001b[0;36mwrapper\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m     61\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     62\u001b[0m         \u001b[1;32mdef\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 63\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mget_method\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     64\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     65\u001b[0m         \u001b[0mwrapper\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__module__\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mowner\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__module__\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\matplotlib\\cbook\\deprecation.py\u001b[0m in \u001b[0;36mwrapper\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    449\u001b[0m                 \u001b[1;34m\"parameter will become keyword-only %(removal)s.\"\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    450\u001b[0m                 name=name, obj_type=f\"parameter of {func.__name__}()\")\n\u001b[1;32m--> 451\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    452\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    453\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\matplotlib\\axis.py\u001b[0m in \u001b[0;36m_set_ticklabels\u001b[1;34m(self, labels, fontdict, minor, **kwargs)\u001b[0m\n\u001b[0;32m   1791\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mfontdict\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1792\u001b[0m             \u001b[0mkwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfontdict\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1793\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_ticklabels\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mminor\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mminor\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1794\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1795\u001b[0m     \u001b[1;33m@\u001b[0m\u001b[0mcbook\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_make_keyword_only\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"3.2\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"minor\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\matplotlib\\axis.py\u001b[0m in \u001b[0;36mset_ticklabels\u001b[1;34m(self, ticklabels, minor, **kwargs)\u001b[0m\n\u001b[0;32m   1712\u001b[0m             \u001b[1;31m# remove all tick labels, so only error for > 0 ticklabels\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1713\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlocator\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlocs\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m!=\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mticklabels\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mticklabels\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m!=\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1714\u001b[1;33m                 raise ValueError(\n\u001b[0m\u001b[0;32m   1715\u001b[0m                     \u001b[1;34m\"The number of FixedLocator locations\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1716\u001b[0m                     \u001b[1;34mf\" ({len(locator.locs)}), usually from a call to\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: The number of FixedLocator locations (5), usually from a call to set_ticks, does not match the number of ticklabels (4)."
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#绘制柱形图\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "#导入文件\n",
    "df1=pd.read_excel('C:\\\\Users\\\\Administrator\\\\Desktop\\\\datas.xls')\n",
    "labels=df1['地区']\n",
    "sizes=list(df1['销售额'])\n",
    "dataLength=len(sizes) #数据长度\n",
    "           \n",
    "# angles数组把圆周等分为dataLength份\n",
    "angles = np.linspace(0,#数组第一个数据\n",
    "2*np.pi,#数组最后一个数据\n",
    "dataLength,#数组中数据数量\n",
    "endpoint=False)#不包含终点\n",
    "sizes.append(sizes[0])\n",
    "\n",
    "angles = np.append(angles, angles[0])#闭合\n",
    "#绘制雷达图\n",
    "plt.polar(angles,#设置角度\n",
    "sizes,#设置各角度上的数据\n",
    "'rv--',#设置颜色、线型和端点符号\n",
    "linewidth=2)#设置线宽\n",
    "           \n",
    "#设置角度网格标签\n",
    "plt.thetagrids(angles*180/np.pi,\n",
    "labels,\n",
    "fontproperties='simhei')\n",
    "           \n",
    "#填充雷达图内部\n",
    "plt.fill(angles,\n",
    "sizes,\n",
    "facecolor='b',\n",
    "alpha=0.6)\n",
    "                                         \n",
    "#图形展示\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
